---

name: twitter-intel description: Twitter keyword search, monitoring, and trend analysis via GraphQL metadata: {"openclaw": {"emoji": "🔍", "os": ["darwin", "linux"], "requires": {"bins": ["python3"], "env": ["TWITTER_COOKIES_PATH"]}}}

Twitter Intel — Keyword Search & Trend Monitor

Search Twitter by keyword, collect high-engagement tweets, analyze trends over time, and generate structured reports. Powered by rnet_twitter.py GraphQL search (no browser automation needed).

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Architecture

Phase 1: On-demand Search (user-triggered)
  User says "search OpenAI on twitter" -> search -> filter -> report

Phase 2: Keyword Monitoring (cron-driven)
  Config defines keywords -> scheduled search -> diff with last run -> alert on new high-engagement tweets

Phase 3: Trend Analysis (on-demand or weekly)
  Aggregate saved searches -> group by week -> detect topic shifts -> generate narrative

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Prerequisites

# Install rnet (Rust HTTP client with TLS fingerprint emulation)
pip install "rnet>=3.0.0rc20" --pre

# Required files:
# 1. rnet_twitter.py — lightweight async Twitter GraphQL client
#    Get it: https://github.com/PHY041/rnet-twitter-client
# 2. twitter_cookies.json — your auth cookies
#    Format: [{"name": "auth_token", "value": "..."}, {"name": "ct0", "value": "..."}]
#    Get cookies: Chrome DevTools → Application → Cookies → x.com
#    Cookies expire ~2 weeks. Refresh when you get 403 errors.

Set TWITTER_COOKIES_PATH env var to your cookies file location.

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Phase 1: On-demand Search

When user says "search [keyword] on twitter", "twitter intel [topic]", "find tweets about [X]":

Step 1 — Run Search

import asyncio, os
from rnet_twitter import RnetTwitterClient

async def search(query, count=200):
    client = RnetTwitterClient()
    cookies_path = os.environ.get("TWITTER_COOKIES_PATH", "twitter_cookies.json")
    client.load_cookies(cookies_path)
    tweets = await client.search_tweets(query, count=count, product="Top")
    return tweets

Search modes:

Mode	product=	Use case
High-engagement	"Top"	Find influential tweets, content analysis
Real-time	"Latest"	Monitor breaking discussions, live tracking

Useful Twitter search operators:

Operator	Example	Effect
lang:en	OpenAI lang:en	English only
since: / until:	since:2026-01-24 until:2026-02-24	Date range
-filter:replies	OpenAI -filter:replies	Original tweets only
min_faves:N	min_faves:50	Minimum likes (only works with Latest)
from:	from:karpathy	Specific author
"exact"	"AI agent"	Exact phrase

Step 2 — Filter & Enrich

After raw search, filter for quality:

filtered = [
    t for t in tweets
    if keyword.lower() in t["text"].lower()
    and (t["favorite_count"] >= 10 or t["retweet_count"] >= 5)
    and not t["is_reply"]
]

Step 3 — Report

Output a structured summary:

## Twitter Intel: [keyword]
**Period:** [date range] | **Tweets found:** N | **After filter:** N

### Top Tweets (by engagement)
1. @author (X likes, Y RTs, Z views) — date
   "tweet text..."
   [link]

### Key Themes
- Theme 1: [description] (N tweets)
- Theme 2: [description] (N tweets)

### Notable Authors
| Author | Followers | Tweets in set | Total engagement |

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Phase 2: Keyword Monitoring (Cron)

Config File

{
  "monitors": [
    {
      "id": "my-product-en",
      "query": "MyProduct lang:en -filter:replies",
      "product": "Top",
      "count": 100,
      "min_likes": 10,
      "alert_threshold": 100,
      "enabled": true
    }
  ]
}

State File

{
  "my-product-en": {
    "last_run": "2026-02-24T12:00:00Z",
    "last_tweet_ids": ["id1", "id2"],
    "total_collected": 450
  }
}

Cron Workflow

1. Read config -> iterate enabled monitors
2. For each monitor:
   • Run search_tweets(query, count, product)
   • Filter by min_likes
   • Diff against last_tweet_ids -> find NEW tweets only
   • If any new tweet has favorite_count >= alert_threshold -> immediate alert
   • Save all new tweets to daily file {monitor_id}/YYYY-MM-DD.json
   • Update state file
3. Send summary notification (if there are new notable tweets)

---

Phase 3: Trend Analysis

When user says "analyze twitter trend for [keyword]", "twitter trend report":

1. Load all saved daily files from {monitor_id}/
2. Group tweets by week
3. For each week, extract:
   • Total tweet count + total engagement
   • Top 5 tweets by likes
   • Dominant themes (use LLM to categorize)
   • New authors that appeared
   • Sentiment shift
4. Generate a week-by-week narrative

---

Commands

User Says	Agent Does
/twitter-intel [keyword]	Search + filter + report (Top, 200 tweets)
/twitter-intel "[phrase]" --latest	Search Latest mode
monitor "[keyword]" on twitter	Add to monitoring config
twitter intel status	Show all active monitors + last run
twitter trend report [keyword]	Analyze saved data, generate trend narrative
refresh twitter cookies	Guide user through cookie refresh

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Technical Notes

• SearchTimeline requires POST (GET returns 404) — handled by rnet_twitter.py
• GraphQL query IDs rotate — if search returns 404, re-extract from https://abs.twimg.com/responsive-web/client-web/main.*.js
• Rate limits: ~300 requests/15min window. With 20 tweets per page, 200 tweets = 10 requests. Safe for cron every 4 hours.
• Cookie lifetime: auth_token expires after ~2 weeks. Monitor for 403 errors.